Increasing popularity of 3D videos calls for new methods to ease the conversion process of existing monocular video to stereoscopic or multi-view video. A popular way to convert video is given by depth image-based rendering methods, in which a depth map that is associated with an image frame is used to generate a virtual view. Because of the lack of knowledge about the 3D structure of a scene and its corresponding texture, the conversion of 2D video, inevitably, however, leads to holes in the resulting 3D image as a result of newly-exposed areas. The conversion process can be altered such that no holes become visible in the resulting 3D view by superimposing a regular grid over the depth map and deforming it. In this paper, an adaptive image warping approach as an improvement to the regular approach is proposed. The new algorithm exploits the smoothness of a typical depth map to reduce the complexity of the underlying optimization problem that is necessary to find the deformation, which is required to prevent holes. This is achieved by splitting a depth map into blocks of homogeneous depth using quadtrees and running the optimization on the resulting adaptive grid. The results show that this approach leads to a considerable reduction of the computational complexity while maintaining the visual quality of the synthesized views.

A new approach to planning and goal-directed behavior has recently been proposed using probabilistic inference in a graphical model that represents states, actions, constraints and goals of the future to infer appropriate actions and controls. The approach has led to new algorithms on the control and trajectory optimization level as well as for high-level rule-based planning in relational domains. In this paper we integrate these methods to a coherent control, trajectory optimization, and action planning architecture, using the principle of planning by inference across all levels of abstractions. Our scenario is a real blocks world: using a 14DoF Schunk arm and hand with tactile sensors and a stereo camera, the goal is to manipulate a set of objects on the table in a goal-oriented way. For high-level reasoning, we learn relational rule-based models from experience in simulation.

A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentation which combines two methods for this integration: a Conditional Random Field (CRF) which couples to local image features and an image classification method which considers global features. The CRF follows the approach of Reynolds & Murphy (2007) and is based on an unsupervised multi scale pre-segmentation of the image into patches, where patch labels correspond to the random variables of the CRF. The output of the classifier is used to constraint this CRF. We demonstrate and compare the approach on a standard semantic segmentation data set.